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Agriculture
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Advancements in Genotype Technology and Their Breeding Applications
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Advancements in Genotype Technology and Their Breeding Applications

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Genetics, Genomics and Breeding".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2472

Special Issue Editor

Dr. Ju-Kyung Yu

E-Mail Website
Guest Editor
Department of Crop Science, Chungbuk National University, Cheongju, Republic of Korea
Interests: crop breeding; crop genetics and genomics; seed production; genotyping technology; soybeans; legumes

Special Issue Information

Dear Colleagues,

Crop breeding has witnessed numerous advancements throughout history, evolving from conventional selective breeding to modern techniques based on molecular markers. The integration of molecular markers has revolutionized the speed and accuracy of plant genetic analysis for the purpose of crop improvement. Furthermore, revolutionary next-generation sequencing (NGS) technology has significantly enhanced crop breeding, evolving into a powerful tool for generating genomic data. NGS technologies introduce innovative tools and concepts that can enhance the accuracy and effectiveness of crop breeding. This includes the development of cost-efficient, high-throughput genotyping technologies as well as their diverse applications in sustainable agriculture.

Systematic data analytics within genotyping approaches—based on principles, applications, and decision scenarios—along with supporting software have revealed that the revolution in genotyping technology has resulted in an explosion of data. This data expansion has driven a breakthrough in integrating artificial intelligence with automation, enabling plant breeders to genotype a large number of samples within a short period of time. This is crucial for implementing genome-wide association studies (GWAS) and genomic selection (GS), paving the way for next-generation breeding programs.

This Special Issue focuses on discussing technological advancements associated with breeding during the big data era, including breeding models, genotyping technologies, and future intelligent breeding. Therefore, the articles included will highlight the potential of smart breeding technologies driven by advances in genotyping/sequencing technology along with advanced data analytics tools.

Dr. Ju-Kyung Yu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genotyping
  • next-generation sequencing (NGS)
  • molecular markers
  • genotyping by sequencing (GbS)
  • marker-assisted breeding
  • genomics assisted breeding
  • genome-wide association studies
  • genomic selection
  • AI-based data analytics
  • phenomics

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Published Papers (3 papers)

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Research

23 pages, 4619 KiB  
Article
HGATGS: Hypergraph Attention Network for Crop Genomic Selection
by Xuliang He, Kaiyi Wang, Liyang Zhang, Dongfeng Zhang, Feng Yang, Qiusi Zhang, Shouhui Pan, Jinlong Li, Longpeng Bai, Jiahao Sun and Zhongqiang Liu
Agriculture 2025, 15(4), 409; https://doi.org/10.3390/agriculture15040409 - 15 Feb 2025
Viewed by 593
Abstract
Many important plants’ agronomic traits, such as crop yield, stress tolerance, and other traits, are controlled by multiple genes and exhibit complex inheritance patterns. Traditional breeding methods often encounter difficulties in dealing with these traits due to their complexity. However, genomic selection (GS), [...] Read more.
Many important plants’ agronomic traits, such as crop yield, stress tolerance, and other traits, are controlled by multiple genes and exhibit complex inheritance patterns. Traditional breeding methods often encounter difficulties in dealing with these traits due to their complexity. However, genomic selection (GS), which utilizes high-density molecular markers across the entire genome to facilitate selection in breeding programs, excels in capturing the genetic variation associated with these traits. This enables more accurate and efficient selection in breeding. The traditional crop genome selection model, based on statistical methods or machine learning models, often treats samples as independent entities while neglecting the abundance latent relational information among them. Consequently, this limitation hampers their predictive performance. In this study, we proposed a novel crop genome selection model based on hypergraph attention networks for genomic prediction (HGATGS). This model incorporates dynamic hyperedges that are designed based on sample similarity to validate the efficacy of high-order relationships between samples for phenotypic prediction. By introducing an attention mechanism, it assigns weights to different hyperedges and nodes, thereby enhancing the ability to capture kinship relationships among samples. Additionally, residual connections are incorporated between hypergraph convolutional layers to further improve model stability and performance. The model was validated on datasets for multiple crops, including wheat, corn, and rice. The results showed that HGATGS significantly outperformed traditional statistical methods and machine learning models on the Wheat 599, Rice 299, and G2F 2017 datasets. On Wheat 599, HGATGS achieved a correlation coefficient of 0.54, a 14.9% improvement over methods like R-BLUP and BayesA (0.47). On Rice 299, HGATGS reached 0.45, a 66.7% increase compared to other models like R-BLUP and SVR (0.27). On G2F 2017, HGATGS attained 0.88, slightly surpassing other models like R-BLUP and BayesA (0.87). We conducted ablation experiments to compare the model’s performance across three datasets, and found that the model integrating hypergraph attention and residual connections performed optimally. Subsequent comparisons of the model’s prediction performance with dynamically selected different k values revealed optimal performance when K = (3,4). The model’s prediction performance was also compared across different single nucleotide polymorphisms (SNPs) and sample sizes in various datasets, with HGATGS consistently outperforming the comparison models. Finally, visualizations of the constructed hypergraph structures showed that certain nodes have high connection densities with hyperedges. These nodes often represent varieties or genotypes with significant impacts on traits. During feature aggregation, these high-connectivity nodes contribute significantly to the prediction results and demonstrate better prediction performance across multiple traits in multiple crops. This demonstrates that the method of constructing hypergraphs through correlation relationships for prediction is highly effective. Full article
(This article belongs to the Special Issue Advancements in Genotype Technology and Their Breeding Applications)
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21 pages, 24056 KiB  
Article
A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network
by Longpeng Bai, Kaiyi Wang, Qiusi Zhang, Qi Zhang, Xiaofeng Wang, Shouhui Pan, Liyang Zhang, Xuliang He, Ran Li, Dongfeng Zhang and Yanyun Han
Agriculture 2025, 15(4), 358; https://doi.org/10.3390/agriculture15040358 - 7 Feb 2025
Viewed by 653
Abstract
The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional [...] Read more.
The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G × E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G × E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies–Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration. Full article
(This article belongs to the Special Issue Advancements in Genotype Technology and Their Breeding Applications)
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18 pages, 2643 KiB  
Article
Genetic Diversity Patterns Within and Among Varieties of Korean Italian Ryegrass (Lolium multiflorum) and Perennial Ryegrass (Lolium perenne) Based on Simple Sequence Repetition
by Dong-Geon Nam, Eun-Seong Baek, Eun-Bin Hwang, Sang-Cheol Gwak, Yun-Ho Lee, Seong-Woo Cho, Ju-Kyung Yu and Tae-Young Hwang
Agriculture 2025, 15(3), 244; https://doi.org/10.3390/agriculture15030244 - 23 Jan 2025
Viewed by 652
Abstract
Italian ryegrass (Lolium multiflorum, IRG) and perennial ryegrass (Lolium perenne L., PRG) are widely cultivated as forage grasses in Korea using heterogeneous and polycross techniques, which promote genetic diversity within varieties. However, their genetic diversity patterns in Korea remain underexplored. [...] Read more.
Italian ryegrass (Lolium multiflorum, IRG) and perennial ryegrass (Lolium perenne L., PRG) are widely cultivated as forage grasses in Korea using heterogeneous and polycross techniques, which promote genetic diversity within varieties. However, their genetic diversity patterns in Korea remain underexplored. This study evaluated the genetic diversity of IRG (eight varieties, including one exotic) and PRG (two exotic varieties) using 66 simple sequence repeat (SSR) markers. Across 87 samples (nine IRG and two PRG varieties), 655 alleles were identified, averaging 9.9 per locus. Key genetic parameters included heterozygosity (0.399), observed heterozygosity (0.675), fixation index (0.4344), and polymorphic informative content (0.6428). The lowest within-variety genetic distance was observed in ‘Hwasan 104ho’ (0.469), while ‘IR901’ had the highest (0.571). Between varieties, the closest genetic distance was between ‘Greencall’ and ‘Greencall 2ho’ (0.542), and the furthest was between ‘Kowinmaster’ and ‘Aspire’ (0.692). Molecular variance analysis showed 90% variation within varieties and 10% among varieties. Five clusters (I–V) were identified, with cluster I primarily including diploid IRG varieties and the tetraploid ‘Hwasan 104ho.’ Structural analysis differentiated diploid from tetraploid varieties (K = 2) and further separated tetraploid IRG and PRG (K = 3). Principal component analysis confirmed these groupings, with ‘Greencall’ and ‘Greencall 2ho’ exhibiting the closest genetic distance (0.227) and ‘Greencall’ and ‘Aspire’ the furthest (0.384). These findings provide a foundational resource for marker-assisted breeding to improve agronomic traits and enhance the efficiency of ryegrass breeding programs. Full article
(This article belongs to the Special Issue Advancements in Genotype Technology and Their Breeding Applications)
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